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CHARLES UNIVERSITY
FACULTY OF SOCIAL SCIENCES
Institute of Economic Studies
Trends and Patterns of Meat Consumption
in The European Union
Bachelor's Thesis
Prague 2021
CHARLES UNIVERSITY
FACULTY OF SOCIAL SCIENCES
Institute of Economic Studies
Trends and Patterns of Meat Consumption in The
European Union
Bachelor's Thesis
Author of the Thesis: Matěj Teiml
Study programme: Economics and Finance
Supervisor: Petr Pleticha M.Sc.
Year of the defence: 2021
Length of the Thesis: 51 151
Declaration
1. I hereby declare that I have compiled this thesis using the listed literature and
resources only.
2. I hereby declare that my thesis has not been used to gain any other academic title.
3. I fully agree to my work being used for study and scientific purposes.
In Prague on 27. 7. 2021 Matěj Teiml
Abstract
The goal of this thesis is to research the consumption of meat in the European
Union and to look for trends and patterns regarding the consumption. We will take
advantage of the panel data structure and use our own balanced panel data set to
properly verify and quantify the relationship of macroeconomic, and country
specific variables with annual meat consumption per capita. We used data covering
the consumption of meat in the European Union member countries in the year of
2018, with data ranging from 2000 to 2018. All data in our thesis are annual and
aggregated at the level of single European Union member countries. We used
standard panel data analysis tools to reach statistically significant results to
recognize and estimate the important determinants of meat consumption in the
European Union
Abstrakt
Cílem této práce je prozkoumat spotřebu masa v Evropské Unii a pokusit se
zmapovat její vzorce a trendy. Využijeme strukturu panelových dat a použijeme
vlastní panelový data set, k řádnému ověření a kvantifikaci vztahu
makroekonomických a pro jednotlivé země specifických proměnných s roční
spotřebou masa na obyvatele. Zkoumali jsme spotřebu masa v členských zemích
Evropské unie pro rok 2018, přičemž jsme využili data v letech 2000 až 2018.
Všechna data v naší práci jsou roční a agregovaná na úrovni jednotlivých
členských zemí Evropské unie. K dosažení statisticky významných výsledků
jsme použili standartní nástroje pro panelovou analýzu, rozpoznali a odhadli jsme
důležité klíčové faktory ovlivňující spotřebu masa v Evropské Unii
Keywords
Meat, Bovine meat, Poultry meat, Pig meat, demand of meat, panel data analysis
Klíčová slova
Maso, Hovězí maso, Drůbeží maso, Vepřové maso, Poptávka po mase, Panelová
analýza dat
Title
Trends and Patterns of Meat Consumption in The European Union
Název práce
Trendy a vzorce ve spotřebě masa v EU
1
Table of Contents
TABLE OF CONTENTS .......................................................................................................................... 1
INTRODUCTION ..................................................................................................................................... 3
1. LITERATURE REVIEW ................................................................................................................ 4
2. DATA ................................................................................................................................................ 7
2.1. MEAT CONSUMPTION ............................................................................................................. 7
2.2. POPULATION .............................................................................................................................. 7
2.3. UNEMPLOYMENT RATE ......................................................................................................... 8
2.4. LABOUR FORCE PARTICIPATION RATE ........................................................................... 8
2.5. FEMALE LABOUR FORCE PARTICIPATION RATE ......................................................... 8
2.6. REAL AVERAGE WAGE ........................................................................................................... 8
2.6.1. GROSS AVERAGE WAGE .................................................................................................... 8
2.6.2. HARMONISED INDEX OF CONSUMER PRICES (HICP) .............................................. 8
2.7. GDP PER CAPITA ....................................................................................................................... 9
2.8. EDUCATION ................................................................................................................................ 9
2.9. PRICES .......................................................................................................................................... 9
2.10. SHARE OF INCOME SPENT ON FOOD ................................................................................. 9
2.11. RELIGION .................................................................................................................................... 9
2.12. URBANIZATION ......................................................................................................................... 9
3. METHODOLOGY AND EMPIRICAL MODELS ..................................................................... 10
3.1. PANEL DATA INTRODUCTION ............................................................................................ 10
3.2. POOLED OLS MODEL ............................................................................................................ 10
3.3. FIXED EFFECTS AND RANDOM EFFECTS MODEL ....................................................... 11
3.4. TEST OF POOLABILITY ........................................................................................................ 11
3.5. TEST OF RANDOM AND TIME-FIXED EFFECTS............................................................. 12
3.6. ELASTICITY .............................................................................................................................. 12
3.7. MODEL OF TOTAL MEAT CONSUMPTION ...................................................................... 12
3.8. LOG-LOG/LEVEL MODELS OF SPECIFIC MEAT CONSUMPTIONS .......................... 13
4. EMPIRICAL RESULTS ............................................................................................................... 15
4.1. MODEL OF TOTAL MEAT CONSUMPTION ...................................................................... 15
4.1.1. POOLED OLS MODEL OF TOTAL CONSUMPTION ................................................... 15
4.1.2. ALL VARIABLES MODEL ................................................................................................. 16
4.1.3. FIXED AND RANDOM EFFECTS MODEL OF TOTAL MEAT CONSUMPTION .... 16
4.2. TRANSFORMED MODEL OF TOTAL MEAT CONSUMPTION ...................................... 18
4.2.1. SPECIFICATION OF OUR TRANSFORMED MODEL .................................................. 18
4.2.2. RESULTS OF OUR TRANSFORMED MODEL REGARDING TOTAL MEAT
CONSUMPTION ..................................................................................................................................... 19
4.3. MODEL OF PORK MEAT CONSUMPTION ........................................................................ 20
2
4.3.1. SPECIFICATION OF OUR TRANSFORMED MODEL .................................................. 20
4.3.2. RESULTS OF OUR TRANSFORMED MODEL REGARDING PORK MEAT
CONSUMPTION ..................................................................................................................................... 20
4.4. MODEL OF BEEF MEAT CONSUMPTION ......................................................................... 21
4.4.1. SPECIFICATION OF OUR TRANSFORMED MODEL .................................................. 21
4.4.2. RESULTS OF OUR TRANSFORMED MODEL REGARDING BEEF MEAT
CONSUMPTION ..................................................................................................................................... 21
4.5. MODEL OF POULTRY MEAT CONSUMPTION ................................................................ 22
4.5.1. SPECIFICATION OF OUR TRANSFORMED MODEL .................................................. 22
4.5.2. RESULTS OF OUR TRANSFORMED MODEL REGARDING POULTRY MEAT
CONSUMPTION ..................................................................................................................................... 22
5. DIFFICULTIES, EXTENSIONS, AND IMPROVEMENTS ..................................................... 23
5.1. DIFFICULTIES REGARDING DATA .................................................................................... 23
5.2. POSSIBLE EXTENSIONS AND IMPROVEMENTS ............................................................ 23
6. CONCLUSION ............................................................................................................................... 24
BIBLIOGRAPHY .................................................................................................................................... 26
3
Introduction
Palaeontology suggests that most of the food consumed by earliest humans living in
hunter-gatherer societies was meat and that their survival was dependant on successfully
hunting animals. A significant factor influencing the availability of meat was
domestication of animals, which can be traced back to the climax of last glacial period,
which is believed to had happened ca. 10 000 years AD1 or a bit later ca. 8 000 years AD2.
The domestication mainly helped in obtaining the meat, but humans learned to use them
as beasts of burden as well. The main boom of meat production began at the start of the
20th century with industrialization of agriculture.
Humans consume a lot of different types of meat coming from various animal
species.3 The most interesting animals eaten in a significant volume are: Horses4, Dogs,
Cats5, Guinea Pigs6, Whales and Dolphins7. The type of meat consumed is based on a lot
of factors, such as culture, geological location, income, accessibility, and convenience.8
Animals that were not eaten in the past, are now being massively farmed because they are
more suitable for our consumption because of their large amount of muscles, examples
being: antelopes, zebras, camels and buffalos.9
From the nutritional point of view, meat is a rich source of nutrients important for
our bodies, all muscle tissue contains a lot of protein, essential amino acids, vitamins and
other nutrients essential for our bodies. Meat is also consumed for it´s rich scale of tastes
and interesting textures. There are also some health risks connected to meat consumption
- World Health Organization (WHO) presents evidence that consumption of processed
meat causes colorectal cancer.10 Overconsumption of meat is also linked to higher
mortality rate.11
Modern meat consumption has a lot of environmental adverse effects. It takes 16
730 litres of water to produce 1 kilogram of beef meat. The livestock sector is probably
the greatest source of water pollution on earth, it accounts for 8% of global water usage
and is one of the main contributors to growing antibiotic resistances. Meat consumption
accounts for 14.5% - 51% of the world's greenhouse gas emissions caused by humans.
Another way in which meat consumption affects the environment is the use of land,
almost 75% of deforested land on earth is used for livestock pastures.12 The fact that meat
consumption causes the loss of biodiversity of our planet should not be forgotten here –
up to 60% of global biodiversity loss is caused by diets based on meat.
We can see that meat consumption is an important part of our economy and
impacts not only our lives but our whole planet. The goal of this thesis is to recognize and
estimate the important determinants of meat consumption in the European Union using
panel data analysis, namely the fixed and random effects models. The research results
could be used to predict meat consumption in upcoming years or by European
Commission’s policymakers when designing policies regarding the meat market. We will
1 Lawrie, Ledward, Lawrie’s Meat Science - 7th Edition. 2 ‘Domestication Timeline | AMNH’. 3 Lawrie, Ledward, Lawrie’s Meat Science - 7th Edition. 4 Alan, The Oxford Companion to Food. 5 Podberscek, ‘Good to Pet and Eat: The Keeping and Consuming of Dogs and Cats in South Korea’. 6 ‘A Guinea Pig for All Tastes and Seasons’. 7 ‘WHALING IN LAMALERA-FLORES’. 8 Gehlhar and Coyle, ‘Global Food Consumption and Impacts on Trade Patterns’. 9 Lawrie, Ledward, Lawrie’s Meat Science - 7th Edition. 10 WHO, ‘Cancer: Carcinogenicity of the Consumption of Red Meat and Processed Meat’. 11 Timothy J., ‘Mortality in Vegetarians and Nonvegetarians: Detailed Findings from a Collaborative Analysis of 5 Prospective
Studies’. 12 The Global Industrial Complex.
4
also try to estimate the income and price elasticities of meat and specific meat types,
namely Pork, Beeg and Poultry. Elasticities can be used by the government when
estimating the effect of taxes, so they can analyse if changing the tax rate will pay off in
the long run by cutting cost either in the health or in the environmental sector.
The thesis follows this structure: Chapter 1 is the literature review, focusing on
the current knowledge of trends, impact on health, determinants and elasticities regarding
meat consumption. Chapter 2 presents data used in our thesis, acknowledges the sources,
and describes our transformations and calculations used during the preparation of our
variables. Chapter 3 describes our methodology and introduces models used in our thesis.
Statistical tests used during our research are also presented there. Chapter 4 contains our
empirical results, estimates of our models are also presented and interpreted here. Chapter
5 contains the difficulties and possible extensions of our thesis. Chapter 6 is the last
chapter and it contains the conclusion of our work.
1. Literature review
Global aggregate consumption of meat rose by 60% between 1990 and 2009, this increase
is of course driven by the increase of the world’s population, however consumption per
capita also rose by almost 25% (from 34 to 42 kg per capita), this suggests that meat
consumption is influenced by other factors.13
The most important factor seems to be the rise of incomes in countries that are
still developing, meat consumption grew threefold in those countries compared to fully
developed countries between 1970s and mid 1990s, this was caused by the decline of real
prices, different income growth, urbanisation, globalisation and trade options being more
liberal.1415
There is no doubt that the consumption of meat is on the rise in recent years. Meat
consumption in fully-developed countries with high income is stagnant or at decline,
while the consumption of meat is increasing rapidly in countries with middle-income that
are still developing, low income countries that are still undeveloped have low and
stagnant meat consumption.16
According to Bennet’s law17, the diet of population changes from starchy foods to
richer diet consisting of fruit, dairy, vegetable and meat when their wealth increases18, the
magnitude of change depends of the relative costs of mentioned products. Other
researchers have used statistical approach based on relationship such as Bennet’s law and
the expected economic growth in the future. Meat consumption increase of 100% between
the years 2005 and 2050 was predicted by those researchers.19
13 Maeve et al., ‘Meat Consumption: Trends and Quality Matters’. 14 Delgado, ‘Rising Consumption of Meat and Milk in Developing Countries Has Created a New Food
Revolution’. 15 Maeve et al., ‘Meat Consumption: Trends and Quality Matters’. 16 Godfray et al., ‘Meat Consumption, Health, and the Environment’. 17 Popkin, ‘The Nutrition Transition and Its Health Implications in Lower-Income Countries’. 18 Tilman et al., ‘Global Food Demand and the Sustainable Intensification of Agriculture’. 19 Valin et al., ‘The Future of Food Demand’.
5
Figure 1.1: Total meat consumption
source: Meat consumption, health, and the environment, Godfray et al.
The strongest negative effect of high meat consumption on health is colorectal cancer,
processed meat is classified as carcinogenic by WHO. The International Agency for
Research on Cancer estimates that 34 000 cancer deaths each year are caused by diet
based on processed meat. Red meat is classified as probably cancerous to human bodies,
should the relationship be proven to be causal, then diets based on red meat could be
responsible for up to 50 000 cancer deaths each year.20
Huge multinational level study European Prospective Investigation into Cancer
and Nutrition (EPIC) with 521 000 test subjects suggests significant relationship of meat
consumption and mortality caused by cancer and cardiovascular diseases.21 Metanalysis
from five studies reported mortality ratios for: fisheaters (0,82), vegetarians (0,84),
occasional meat eaters (0,84), regular meat eaters (1,00), and vegans (1,00) suggesting
that consuming either a lot of meat or almost no meat is tied to bigger mortality rate.22
Research from western countries suggests that specifically red meat and processed
are tied to higher mortality rates, on the other hand Poultry meat has no ties to higher
mortality rates. Part of those results could be caused by the fact that red meat consumption
is often connected with other risk factors such as smoking, obesity and alcohol
consumption, unfortunately the information to statistically remove other factors are not
available.23
Rapid increase of meat consumption is also suspected to be the reason behind
imbalanced intakes of meat and cholesterol of humans.24
The World Cancer research Fund recommends the maximum of 500g of red meat
per week for people that eat red meat, so that the global average consumption is below
20 WHO, ‘Cancer: Carcinogenicity of the Consumption of Red Meat and Processed Meat’. 21 European Prospective Investigation into Cancer and Nutrition. 22 Timothy J., ‘Mortality in Vegetarians and Nonvegetarians: Detailed Findings from a Collaborative
Analysis of 5 Prospective Studies’. 23 Godfray et al., ‘Meat Consumption, Health, and the Environment’. 24 Horowitz, Putting Meat on the American Table.
6
300g of red meat per week, the Global Burden of Disease initiative suggests that the
maximum intake of red meat should be 100g per week to reduce the burden of meat
consumption.25
Another health risk connected with the consumption of meat is the fact that meat
can be a source of infections harmful to humans.26
Now we will present the current relevant studies regarding the elasticities of meat.
Craig A. Gallet is the author of two great meta-analyses regarding meat and elasticities.
The first one (2010) is based on 393 studies with a total of 3357 estimates of factors
influencing income elasticities regarding meat. The global average of the income
elasticity of meat is 0,9. If we divide the meat into specific categories as used in our thesis
(Beef, Pork and Poultry meat) we can observe that elasticity is lower for Poultry and Pork,
this means that the consumption of pork and poultry meat is less sensitive to changes in
income than the consumption of beef. From the regional point of view the only notable
and statistically significant difference is in Australia, the estimate is roughly -0,45
meaning that the meat consumption in Australia is less dependant on the income, than in
the rest of the world. 27
The second on (2012) is based on 362 studies with total of 3357 estimates of
factors influencing price elasticities regarding meat. The focus of this study are the
regional differences.
Figure 1.2: Price elasticities in the world
North America Asia Europe
Beef -1,084 -0,981 -0,981
Pork -0,913 -0,809 -0,936
Poultry -0,743 -0,845 -0,851
Meat composite -0,964 -0,848 -0,831
The table above presents the predictions of price elasticities in the meta-analysis.28
We will now focus on the main determinants and drivers of meat consumption. Meat
consumption is believed to be tied to standard of living.29 York and Gossard found clear
positive effect of GDP per capita on meat consumption.30
Living in cities has strong relationship with people consuming more meat, people
living in cities tend to have different lifestyles and diets. Data from eight European
countries suggest that urbanisation is strongly correlated with increase in meat
consumption namely Poultry and Pork. Beef meat consumption seems to be unaffected
by the degree of urbanization. 31
Regmi and Gehlhar (2001) assessed the relationship of age and meat consumption,
saying that old people tend to eat less meat than young people. They also linked meat
consumption negatively with higher levels of education.32
The unemployment rate could also impact meat consumption, unemployed people are
expected to have less money available. Another work-related demographic variable is
25 Godfray et al., ‘Meat Consumption, Health, and the Environment’. 26 Mann and Truswell, Essentials of Human Nutrition. 27 Gallet, ‘The Income Elasticity of Meat’. 28 Gallet, ‘A Meta-Analysis of the Price Elasticity of Meat’. 29 Kanerva, ‘Meat consumption in Europe: issues, trends and debates’. 30 York and Gossard, ‘Cross-National Meat and Fish Consumption: Exploring the Effects of
Modernization and Ecological Context’. 31 Kanerva, ‘Meat consumption in Europe: issues, trends and debates’. 32 Regmi and Gehlhar, ‘Consumer Preferences and Concerns Shape Global Food Trad’.
7
women's labour force participation, the relationship is that the more women work the less
time and means they have for preparation of more complicated dishes containing meat.33
Last determinant of meat consumption acknowledged in the literature is the
percentage of income people spent on food. The direction of effect of this variable
depends on the development of the country in question, it has a positive relationship to
meat consumption in countries that are still developing, on the other hand it has negative
effect in countries that are already fully developed. This could be explained in a way that
when one´s food budget reaches a certain benchmark they halt the increase in their meat
consumption and buy more luxurious goods instead.
2. Data
2.1. Meat Consumption
The key segment of data regarding sales of meat was obtained from Food and Agriculture
Organization Corporate Statistical Database (FAOSTAT) maintained by the Food and
Agriculture Organization (FAO). FAOSTAT provides data in a time-series format,
starting from 1961, for 245 countries. The data was taken from the food balances section,
which tracks detailed information about country's food supply in a specified time period.
Although sales themselves are not part of the data, we decided to calculate them
using other variables from the food balances section. Sales were calculated in the
following way:
Sales = Production + Imports – Exports + changes in stock – losses
Losses are the amount of the commodity lost as waste during all stages between
production and households.
This approximation of sales data is sufficient for my thesis. There are some
imperfections regarding this approach for example the fact, that FAO did not collect data
about losses at the consumption stage until recent years, so the real figures of sales are
lower than the data we will be using. Another issue is that FAO includes even inedible
parts of animals such as bones in its figures, so approximately 15% should be deducted
to get the actual volume of eaten meat. we assume that all data are impacted in the same
magnitude by these issues and will use the figures obtained by methodology stated above.
2.2. Population
To obtain the data regarding per capita consumption, it was necessary to obtain accurate
population values. We decided to use The United Nations Economic Commission for
Europe (UNECE) as our source for population figures, because it captures the values, we
need at all points in time for all of the countries in my research. Since the countries we
study did not undergone any major change regarding their population structure during the
years, we can use this data without any approximations.
33 Kanerva, ‘Meat consumption in Europe: issues, trends and debates’.
8
2.3. Unemployment rate
Unemployment rate is the share in percentages of the unemployed in the labour force. we
used UNECE as our source for unemployment rate because the data was easily accessible
and without missing values. UNECE compiles data regarding unemployment using
national and international official sources such as European Statistical Office
(EUROSTAT) or Organisation for Economic Co-operation and Development (OECD).
2.4. Labour force participation rate
Labour force participation rate is calculated as the labour force divided by the total
working-age population. Even thought this variable would be interesting to study, we
decided that variable that expresses the number of “dependants” would be more
interesting. Dependants are people less than 15 years old and people more than 64 years
old. We obtained the data regarding these groups of population from the world bank, the
data tells us what share of population is dependant and in which age group they fall.
2.5. Female labour force participation rate
Female labour force participation rate tells us the share of woman actively participating
in the labour force. we used UNECE as my data source. Namely their huge gender pay
gap section.
2.6. Real Average Wage
To observe the effect income has on meat consumption and to obtain the price elasticities
of meat and its various types, we need real average wage. To shave off the inflation effect
we calculated real average wage using the gross average wage and Harmonised index of
consumer prices (HICP).
𝑅𝑒𝑎𝑙 𝑤𝑎𝑔𝑒 =𝑁𝑜𝑚𝑖𝑛𝑎𝑙 𝑤𝑎𝑔𝑒
𝐻𝐶𝐼𝑃∗ 100
2.6.1. Gross Average Wage
Gross Average Wage covers total monthly wages and salaries before any tax deductions
and social security contributions. we used UNECE as my data source because it covered
most of my needed values unlike other data sources such as OECD or EUROSTAT.
Wages cover total economy and are expressed per full-time equivalent employee.
There were a few missing observations regarding gross average wage, we decided
to approximate the missing values, because we did not want to omit any countries of the
European Union from this thesis. we decided to approximate the missing values from the
growth of gross domestic product (GDP) between the missing year and the closest not
missing value. This solution is not the most accurate one, but it will prevent us from
omitting up to five countries from our thesis.
2.6.2. Harmonised index of consumer prices (HICP)
The Harmonised index of consumer prices (HICP) is an indicator of inflation and price
level used by European Central Bank (ECB). This indicator measures the over time
changes of prices of consumer goods and services acquired by households. we will use
9
harmonised index of consumer prices regarding all items taken from EUROSTAT with
base year of 2015.34
2.7. GDP per capita
We obtained our real GDP per capita from the world bank and transformed it to the base
year of 2015 so that it is synced with our other real variables regarding finance.
2.8. Education
We obtained our data regarding education from EUROSTAT, the data tells us what share
of population attained Tertiary education (bachelor’s degree or better).
2.9. Prices
Our price data were based from International Monetary Fund, unfortunately we had to
use global commodity prices, getting real prices from each country of the European Union
would be difficult. No data source unfortunately collects this data for all the meat types
for a significant time, for the data to be useful in our research.
We used meat CPI to deflate the prices to their real value and transformed them
to more useful metric than cents per pound. We also computed our own variable Meat
Price by weighting the prices of specific meats in respect to the share of quantity
consumed.
2.10. Share of income spent on food
The data regarding percentage of income spent on food was obtained from EUROSTAT,
from the final consumption expenditure of households, by consumption purpose
indicator.
2.11. Religion
Unfortunately, we did not manage to find a reliable and complete data set regarding
religion. We think that religion might have a notable impact on the overall meat
consumption, because some religions forbid the consumption of certain meats or
encourage their followers to consume meat in certain times of celebration. The only data
available were for all member states in year 2015, we decided to not use them because
we cannot assume that Religion did not change during the years in question and because
it would be our only time invariant variable. Main reason why data regarding religion
might be not available could be the fact that it is difficult to track peoples believes and
annual censuses would be needed to obtain reliable data.
2.12. Urbanization
We obtained our data regarding urbanization from the World Bank, the data are collected
and smoothed by the United Nations Population Division. The data refers to the
percentage of population living in urban areas as defined by national statistical offices.
34 ‘Harmonised Index of Consumer Prices (HICP) (Prc_hicp)’.
10
3. Methodology and Empirical Models
3.1. Panel Data Introduction
Thanks to the nature of the available data and to the fact that we for sure have no control
over all significant variables regarding meat consumption, we decided to use panel
analysis to reach consistent and unbiased estimators. 35 We assume correlation over time
for given individuals (countries) with independence over individuals. Models estimating
panel data work around the differences of observed individuals and control the
unobserved heterogeneity.
My dataset is a data series with length equal to:
Number of Countries (28) * Number of Years (19)
We made sure that my dataset will be balanced, meaning that every combination of
Country and Year has a value for every variable used in the model.
The general form of panel data model can be written as:
Yit = xit β + ci + uit t = 1, 2, … , T
Where β are coefficients of variables with cross-sectional observations, ci are country
specific unobserved components and uit are idiosyncratic errors.
There are 3 types of models we can employ when working with panel data –
Pooled OLS model, Fixed Effects (FE) model and Random Effects (RE) model, their
properties will be described in the upcoming chapters along with my tests and decision
regarding choosing the correct one.
3.2. Pooled OLS model
The pooled OLS model disregards the unobserved heterogeneity across countries and the
timeseries aspect of the data. The model is ideal when one selects a different sample for
each time period they observe and assume that no unobserved significant variables change
over time.36 Because this is not my case, we will expect Fixed effects or random effects
models to be the right choice. we will be using a simple F test to test my hypothesis if
selecting FE or RE models will lead to better results.
35 Wooldridge, Introductory Econometrics : A Modern Approach. 36 Wooldridge, Introductory Econometrics : A Modern Approach.
11
3.3. Fixed Effects and Random Effects model
Fixed Effects and Random Effects models are results of two different treatments of
unobserved components. Fixed Effects approach treats c as a parameter on the other hand
Random Effects approach assumes that c is a random variable obtained by random effects
estimation. Wooldridge specifies that random effect is determined by no correlation of
observed explanatory variables and the unobserved effect.37
In the Fixed Effects model every observed individual (country in my case) has its
own intercept term ci and same slope parameters of β as other individuals. The Fixed
Effects model can look like this:
yit = ci + xit β + uit t = 1, 2, … , T
In the Random Effects model ci is assumed to be distributed independently of the
regressors. Each individual has the same interecept term, same slope parameters of β as
other individuals and a composite error term uit = ci + eit. The Random Effects model can
look like this:
yit = xit β + ( ci + eit)
The Fixed effects model will always give consistent estimates, but they might not be the
most efficient.
The Random effects estimator is inconsistent if the appropriate model is the fixed effects
model.
The Random effects estimator is consistent and most efficient if the appropriate model is
random effects model.38
We will resolve the decision between Fixed Effects and Random Effects in the Empirical
results section. We will be deciding based on the Hausman specification test introduced
by Hausman (1978)39. The test calculates if statistically significant difference between
fixed and random effect exists. If we cannot reject the null hypothesis stated below than
both models lead to consistent estimators, but Random effect estimators will be more
efficient.40 On the other hand If we reject the null hypothesis, then Random effects model
is inconsistent and only Fixed effect model should be used in the analysis.
Hausman test hypothesis:
H0: yit = β1xit1+ β0+ … + βkxitk+ci+uit
Where Cov (xitj,ai)=0, for all t = 1, 2, .., T and j = 1, 2, …, k.
H1: Cov (xitj, ai) ≠ 0
3.4. Test of poolability
A poolability test is an F test of the null hypothesis where all Fixed Effects are jointly 0.41
The test compares the simple OLS pooled model with the Fixed model and tests whether
37 Wooldridge, Econometric Analysis of Cross Section and Panel Data, Volume 1. 38 Katchova, ‘Panel Data Models’. 39 Hausman, ‘Specification Tests in Econometrics’. 40 Hausman. 41 ‘SAS Help Center: Poolability Test for Fixed Effects’.
12
the same coefficients apply to every observed individual.42 This test helps us to select the
correct model.
3.5. Test of random and time-fixed effects
We will also be running Lagrange Multiplier Breusch-Pagan tests to tell us if time-fixed
effects and random effects are present so we can choose the best model correctly.
3.6. Elasticity
We are going to estimate elasticites using standard OLS constant elasticity model.43Our
variables will need to be log transformed, so that no further computations are needed.
Income elasticity measures the sensitivity of the quantity demanded to a change in the
real income of consumers if all the other variables are held constant. Based on the value
of income elasticity we can say if the good is inferior (e < 0) or normal (e > 0). Normal
gods are further categorized into necessities (e < 1) and luxury goods (e >1).
Price elasticity measures the change in consumption of a good with respect to the
change in its price, if all other variables are hold equal. Goods with negative price
elasticity are called Giffen goods, their consumption increases when their price increases
and vice versa.
Cross price elasticity measures the responsiveness of the quantity demanded to a
change in real price of another good, all prices and utilities needs to be held constant.
Based on the values of cross price elasticities, we call a pair of goods either complements
or substitutes. If the price of good B rises and the quantity of good A also rises, then A is
a substitute to B. If the quantity demanded would fall, A would be complement to B
instead.
3.7. Model of total meat consumption
We are going to define my model regarding the total meat consumption per capita in the
European Union. The goal of this model is to find the main determinants of meat
consumption, see which variables significantly influence meat consumption and what are
the main factors of meat consumption in the European Union.
We are going to link the variables mentioned in literature such as: average real
wage, unemployment rate, share of young dependants, share of old dependants,
urbanization, female labour force share, GDP per capita, share of population with
university degree, price and percentage average household spending on food to meat
consumption per capita. We need to assume that cultural properties of the population such
as religion or dietary trends such as veganism do not change over time.
The next table presents the description of variables used in the model.
Table 3.1. Total meat consumption variables Variable Description Source Unit Expectation
MeatPC Meat consumption per
capita
FAOSTAT Kg per
capita
Dependent
variable
42 Croissant and Millo, ‘Panel Data Econometrics in R’. 43 Wooldridge, Introductory Econometrics : A Modern Approach.
13
Average real wage Average monthly salary UNECE USD Positive Effect
Unemployment
Share of labour force
without work
UNECE % Negative
Effect
Young % of population that are
less than 15 years old
UNECE % Negative
Effect
Old % of population that are
older than 64 years old
UNECE % Negative
Effect
Urbanization Says if huge % of
population lives in the
cities
The World
Bank
% Positive Effect
Female labour force share Share of females in the
labour force
UNECE % Negative
Effect
GDP per capita GDP per capita The World
Bank
USD per
capita
Positive Effect
Education Share of population with
university degree
Eurostat % Negative
Effect
Meat Price Price of meat International
Monetary
Fund
USD per
Kg
Negative
Effect
Food Percentage Share of income spent on
food by households
Eurostat % Negative
Effect
The Equation of the model:
MeatPCc,t = α + β1 Average real wage c,t + β2 Unemploymentc,t + β3 Young c,t + β4 Oldc,t
+ β5 Urbanization c,t + β6 Female labour force sharec,t + β7 GDP per capita c,t + β8
Education c,t + β9 Meat Pricec,t + β10 Food Percentagec,t +Unobserved effectc + e c,t
The goal of this model is to assess the relationship of general properties of the population
and certain country specific properties with the overall meat consumption in European
Union.
Because no values are time invariant, we will not lose any variables in case of H0
rejection of of the Hausman test.
3.8. Log-log/level Models of specific meat consumptions
In this part of my thesis, we are going to specify my models of specific meat consumptions
in the European Union and also rerun the general consumption model with the
transformations described in this chapter. The goal of this model will be to try to
determinants of consumption of poultry, beef and pork meat. Another goal is to obtain
price, income and cross elasticities of specific meats and meat as a whole.
We are going to link the same variables based on literature and assume the same
assumptions as in the total meat consumption model from the previous chapter. The first
14
difference will be the log transformation of the dependent variable and few explanatory
variables, this will help us in interpreting the results and save us calculations regarding
elasticities, second difference is that we will split the meat consumption variable and meat
price variable into 6 new variables – PorkPC, BeefPC and PoultryPC, Pork Price, Beef
Price and Poultry Price.
The next table presents the description of variables used in the model.
Table 3.2. Specific meat consumption variables Variable Description Source Unit Expectation
MeatPC Meat consumption per
capita
FAOSTAT Kg per
capita
Dependent
variable
PorkPC Pork consumption per
capita
FAOSTAT Kg per
capita
Dependent
variable
BeefPC Beef consumption per
capita
FAOSTAT Kg per
capita
Dependent
variable
PoultryPC Poultry consumption per
capita
FAOSTAT Kg per
capita
Dependent
variable
Log(Average real wage) Average monthly salary UNECE USD Positive Effect
Unemployment
Share of labour force
without work
UNECE % Negative Effect
Young % of population that are
less than 15 years old
UNECE % Negative Effect
Old % of population that are
older than 64 years old
UNECE % Negative Effect
Urbanization Says if huge % of
population lives in the
cities
The World
Bank
% Positive Effect
Female labour force share Share of females in the
labour force
UNECE % Negative Effect
Log(GDP per capita) GDP per capita The World
Bank
USD per
capita
Positive Effect
Education Share of population with
university degree
Eurostat % Negative Effect
Log(Meat Price) Price of meat International
Monetary
Fund
USD per
Kg
Negative/Positive
Effect
Log(Pork Price) Price of meat International
Monetary
Fund
USD per
Kg
Negative/Positive
Effect
15
Log(Beef Price) Price of meat International
Monetary
Fund
USD per
Kg
Negative/Positive
Effect
Log(Poultry Price) Price of meat International
Monetary
Fund
USD per
Kg
Negative/Positive
Effect
Food Percentage Share of income spent on
food by households
Eurostat % Negative Effect
*Dependent variable/ Positive or negative effect when used as explanatory variable.
The Equation of the general model:
Log(MeatPCc,t )= α + β1 log(Average real wage c,t )+ β2 Unemploymentc,t + β3 Young c,t
+ β4Old c,t + β5 Urbanization c,t + β6 Female labour force sharec,t + β7 log(GDP per
capitac,t)+ β8 Education c,t + β9 log(Meat Pricec,t )+ β10 Food Percentagec,t +Unobserved
effectc + e c,t
The Equation of the specific models:
Log(XPCc,t )= α + β1 log(Average real wage c,t )+ β2 Unemploymentc,t + β3 Young c,t +
β4 Oldc,t + β5 Urbanization c,t + β6 Female labour force sharec,t + β7 log(GDP per
capitac,t)+β8Education c,t + β9 log(Pork Pricec,t )+ β10 log(Beef Pricec,t )+ β11 log(Poultry
Pricec,t )+ β12 Food Percentagec,t +Unobserved effectc + e c,t
Where X ∈ {pork, beef, poultry}
This model is a combination of the constant elasticity model and the log-level model. This
means that our obtained estimations of parameters regarding wage and price will be our
elasticities and after being multiplied by 100, the rest of our estimates of parameters will
represent the percentage change in dependant variable if the explanatory variable
increases by 1 unit.44
4. Empirical Results
4.1. Model of total meat consumption
4.1.1. Pooled OLS Model of total consumption
The first model we are going to estimate is going to be estimated with dataset consisting
of 532 annual observations covering period from 2000 to 2018 for 28 countries. Even
though Pooled models ignore the panel structure of data especially the individual effect
for given countries, leading to poor result value, it can be a useful way of checking if the
underlying data is suitable for more advanced analysis.
44 Wooldridge.
16
4.1.2. All Variables Model
The first step of pooled OLS model was running the regression with all variables in our
dataset with suspected effect on our dependant variable (annual meat consumption per
capita).
The All Variables model could explain 36% of the model’s variance. Three
Coefficient were not significantly different from 0, that being Average wage, GDP per
capita and the price of meat. We are not going to omit those variables, because their
significance might improve if we use more efficient models. We did the Breusch-Pagan
Lagrange Multiplier test for random effects to confirm the presence of random effects and
concluded that we should use either fixed or random effects model to obtain the best
results.
4.1.3. Fixed and Random Effects Model of total meat
consumption
In the preceding part of our research, we successfully tested suitability of our data for the
simple pooled OLS model and eligibility of fixed or random effects model. In this part
we are going to test the differences of effects for individual countries using either Fixed
Effects or Random Effects model using the model from previous sections.
The LM Breusch-Pagan test could not reject the existence of time random effects which
means that we should use time-fixed effects.
Our last test will be an F-test, comparing coefficients of time dependant variables
from our adjusted pooled OLS model and Fixed Effects model.
The test results supported the existence of Fixed pooled effects with high
statistical significance.
After evaluating our tests results, we know for sure, that Fixed effects and Random
effects model both contain additional important information and should be used instead
of Pooled OLS model. We are going to use the Hausman test, already discussed in
preceding sections to decide which model is more suitable for our analysis. Hausman test
rejected the null hypothesis, which means that Fixed effects model is the most consistent.
Our Fixed Effects model is going to be estimated using the same variables as our
OLS pooling model discussed in preceding chapters.
Fixed effects model equation:
MeatPCc,t = β1 Average real wage c,t + β2 Unemploymentc,t + β3 Young c,t + β4 Oldc,t +
β5 Urbanization c,t + β6 Female labour force sharec,t + β7 GDP per capita c,t + β8
Education c,t + β9 Meat Pricec,t + β10 Food Percentagec,t +Individual effectc + time
effectt+ e c,t
Table 4.1. Fixed effects model results regarding total meat consumption Coefficient
Fixed effects Std. Error P (>|t|)
Average real wage
0,0032 0,0011 0,00374 **
Unemployment
-0,299 0,127 0,0188 *
Young -1,239 0,243 5.185e-07 ***
17
Old 1,791 0,276 2.2e-10 ***
Urbanization 0,296 0,235 0,209
Female labour force share -1,894 0,307 1.441e-09 ***
GDP per capita -0,00051 0,00147 0,000584 ***
Education -0,306 0,157 0,0532 .
Meat Price -4,381 1,888 0,0207 *
Food Percentage -0,983 0,293 0,000872 ***
R-Squared 0,3571
Note: . p<0,05 ; * p<0,01 ; ** p<0,001 ; *** p < 0
The explanatory power of our Random Effects model is 35,7% of overall variance. The
only variable that was not statistically significant is urbanisation, this surprised us because
the urbanisation rate was proven to affect the consumption of pork and poultry meat
before, we will see if urbanisation will have impact on consumption in next chapters.
Even though average real wage is statistically significant, we expected its
magnitude to be bigger, our estimate can be interpreted as follows: “if average real wage
increases by 1000 dollars, average meat consumption per capita increases by 3,2 kg. This
further confirms the idea that meat consumption is stagnant in developed countries and
that a certain wage threshold exists, once this threshold is overstepped meat consumption
is not going to increase with increasing wages.
Unemployment performed as expected, if the unemployment rate increases by 1%,
the average meat consumption per capita drops by 0,3 kg, this would be consistent with
our expectation that unemployed people have less disposable income and spend money
on cheaper food than meat.
The share of young dependants was statistically significant a performed as we
expected, if the share of young dependants increases by 1%, the average meat
consumption per capita drops by 1,24 kg, this can be explained by two factors, both are
probably correct. Children consume less food in general than average citizens and have
no disposable income and rely on their parents, in other words young dependants do not
earn wage bud still consume meat.
The share of old dependants performed opposed to our expectation, if the share of
old dependants increases by 1%, the average meat consumption per capita increases by
1,791 kg. We thought that older people will consume less meat overall because of lower
income and the fact, that older people consume less food in general then average citizens.
This estimate suggests that then still earn enough to buy as much meat they need or by
the fact that they do not really care about adverse effect of meat consumption and are less
cautious when managing their diet.
The share of females in the labour force was estimated just as we expected – for
every 1% increase in the share, the average meat consumption drops by 1,894 kg. This
could be explained by the fact that women that do not work have more time to prepare
food containing meat that usually needs longer time to prepare.
We expected GDP per capita to have positive impact on meat consumption, but
that was based on studies of global scale. Countries in our thesis are members of the
European union and are already well developed, meaning that they already had their peak
18
of meat consumption and even with rising wealth, the meat consumption is not going to
increase dramatically. This could also suggest that wealthier countries have more money
to make their diets less meat based and better overall.
Our estimate of share of university level education graduates performed in the way
our studied literature suggested, but we expected its magnitude to be slightly higher. if
the share of university level education graduates increases by 1%, the average meat
consumption per capita drops by 0,306 kg, this could support our suspicion that educated
people do overall better and healthier choices regarding their diet.
Real meat price performed as expected, its effect on meat consumption is strongly
negative, if the price of meat rises by a dollar, the average meat consumption drops by
4,381 kg, we will study the relationship of wage and meat consumption more closely in
further parts.
Our last estimate regarding the share of income spent on food, performed as
expected in developed countries such as countries of the European Union. If the share of
income increases by 1%, the consumption drops by 0,983 kg per capita.
Table 4.2. Top and bottom five fixed effects regarding total meat consumption Country
Fixed Effect
Cyprus 0,061945
Spain 0,058138
Germany 0,057169
Poland 0,056259
Austria 0,054839
… …
Malta 0,031785
France 0,031353
Bulgaria
0,029519
Denmark 0,029023
United Kingdom 0,022857
4.2. Transformed model of total meat consumption
4.2.1. Specification of our transformed model
In this chapter we will be using the same data set consisting of 532 annual observations
covering the period from 2000 to 2018 for 28 countries. We will transform the model in
the last chapter to a log-log/level model and use the same fixed effect estimation method
to reach or estimates.
19
The Equation of our model:
Log(MeatPCc,t ) = β1 log(Average real wage c,t )+ β2 Unemploymentc,t + β3 Young c,t +
β4Old c,t + β5 Urbanization c,t + β6 Female labour force sharec,t + β7 log(GDP per
capitac,t)+ β8 Education c,t + β9 log(Meat Pricec,t )+ β10 Food Percentagec,t +Individual
effectc + time effectt+ e c,t
4.2.2. Results of our transformed model regarding
total meat consumption
Table 4.3 Transformed model of total meat consumption Coefficient
Fixed effects Std. Error P (>|t|)
Log(Average real wage)
0,169 0,0489 0,000582 ***
Unemployment
-0,00162 0,00211 0,444
Young -0,0187 0,00354 1,758e-07 ***
Old 0,0228 0,0042 9,054e-08 ***
Urbanization 0,00737 0,00363 0,0426 *
Female labour force share -0,0255 0,00448 2,305e-08 ***
Log(GDP per capita) -0,133 0,0898 0,138
Education -0,00615 0,00232 0,00827 **
Log(Meat Price) -0,184 0,0587 0,00184 **
Food Percentage -0,0214 0,00502 2,435e-05 ***
R-Squared 0,34437
Note: . p<0,05 ; * p<0,01 ; ** p<0,001 ; *** p < 0
The most interesting estimate of our model is urbanization. This time the results of
urbanization are statistically significant. The estimate confirms our suspicion based on
existing literature – if the share of urbanization increases by 1% than the total meat
consumption rises by 0,7%. This confirms that city life leads to higher meat consumption,
the reasons behind this relationship are probably caused by the availability of cut and
prepared meat in supermarket and the overall “faster” lives of people living in cities.
Our estimate of the income elasticity of meat is statistically significant, but quite
lower than the expected value of around 0,9, we will try to explain why in the conclusion
chapter.
Our estimate of price elasticity of meat is -0,184, this is again - lower than
expected, we thought that meat consumption would be more influenced
by real prices. On the other hand, it makes meat a price inelastic good, which was
expected.
20
4.3. Model of Pork meat consumption
4.3.1. Specification of our transformed model
In this chapter we will be using the same data set consisting of 532 annual observations
covering the period from 2000 to 2018 for 28 countries. We will change our dependant
variable to be log(pork consumption per capita). After log transformation of selected
explanatory variables, we will get the estimates using a simple pooled OLS model.
The Equation of our model:
Log(PorkPCc,t )= α + β1 log(Average real wage c,t )+ β2 Unemploymentc,t + β3 Young c,t
+ β4 Oldc,t + β5 Urbanization c,t + β6 Female labour force sharec,t + β7 log(GDP per
capitac,t)+β8Education c,t + β9 log(Pork Pricec,t )+ β10 log(Beef Pricec,t )+ β11 log(Poultry
Pricec,t )+ β12 Food Percentagec,t +Unobserved effectc + e c,t
4.3.2. Results of our transformed model regarding
pork meat consumption
Table 4.4 Log-log/level model of total pork meat consumption Coefficient
Estimate Std. Error P (>|t|)
Intercept
3,573 0,476 2,528e-13 ***
Log(Average real wage)
-0,142 0,0529 0,00753 **
Unemployment
0,00138 0,00273 0,615
Young -0,0421 0,00476 2,2e-16 ***
Old -0,0188 0,00349 1,010e-07 ***
Urbanization -0,00454 0,00124 0,000272 ***
Female labour force share -0,00296 0,00493 0,549
Log(GDP per capita) 0,309 0,0585 1,87e-07 ***
Education -0,0011 0,00274 0,688
Log(Beef Price) -0,131 0,116 0,26
Log(Pork Price) 0,0914 0,0869 0,293
Log(Poultry Price) 0,265 0,134 0,0476 *
Food Percentage -0,00776 0,00498 0,12
R-Squared 0,28096
Note: . p<0,05 ; * p<0,01 ; ** p<0,001 ; *** p < 0
21
Our estimate of the income elasticity of pork meat is statistically significant, but quite
differs from our expectations, our estimate says that if real average wage increases by 1%
then the consumption of pork meat drops by 0,142%, this would mean that pork is an
inferior good and that when the salary of citizens rises, they swap out pork from their diet
to richer and more expensive food.
Price elasticity of pork and cross price elasticity with beef were statistically
insignificant so we can not draw any conclusions from these estimates. On the other hand
the estimate of cross price elasticity with poultry equals 0,265, this means that when the
price of poultry rises by 1%, than the consumption of pork rises by 0,265% we can say
that pork is a substitute of poultry in this case.
4.4. Model of Beef meat consumption
4.4.1. Specification of our transformed model
In this chapter we will be using the same data set consisting of 532 annual observations
covering the period from 2000 to 2018 for 28 countries. We will change our dependant
variable to be log(beef consumption per capita). After log transformation of selected
explanatory variables, we will get the estimates using a simple pooled OLS model.
The Equation of our model:
Log(BeefPCc,t )= α + β1 log(Average real wage c,t )+ β2 Unemploymentc,t + β3 Young c,t
+ β4 Oldc,t + β5 Urbanization c,t + β6 Female labour force sharec,t + β7 log(GDP per
capitac,t)+β8Education c,t + β9 log(Pork Pricec,t )+ β10 log(Beef Pricec,t )+ β11 log(Poultry
Pricec,t )+ β12 Food Percentagec,t +Unobserved effectc + e c,t
4.4.2. Results of our transformed model regarding
Beef meat consumption
Table 4.5 Log-log/level model of total beef meat consumption Coefficient
Estimate Std. Error P (>|t|)
Intercept
-7,04 1,153 1,993e-09 ***
Log(Average real wage)
0,39 0,128 0,00247 **
Unemployment
0,0183 0,00662 0,00596 **
Young 0,029 0,0115 0,0123 *
Old 0,0414 0,00846 1,344e-06 ***
Urbanization 0,00597 0,003 0,0473 *
Female labour force share -0,0153 0,012 0,201
Log(GDP per capita) 0,555 0,142 0,000103 ***
Education -0,015 0,00664 0,0245 *
22
Log(Beef Price) -1,107 0,282 9,9e-05 ***
Log(Pork Price) 0,781 0,211 0,000232 ***
Log(Poultry Price) -0,0934 0,324 0,773
Food Percentage 0,0444 0,0121 0,000262 ***
R-Squared 0,49425
Note: . p<0,05 ; * p<0,01 ; ** p<0,001 ; *** p < 0
Our estimate of the income elasticity of beef meat is statistically significant, the value of
0,39 means that if real wages increase by 1% beef meat consumption will rise by 0,39%.
We expected slightly larger estimate, we will discuss this in the conclusion chapter.
Our estimate of price elasticity of beef meat is -1,107, this value is interesting, it
supports the findings of existing literature and is in line of our expectations. This would
categorize beef as a relatively price elastic good.
The estimate of cross price elasticity with pork equals 0,781, this means that when
the price of pork rises by 1%, than the consumption of beef rises by 0,781% we can say
that beef is a substitute of pork in this case.
Since the our estimate of cross price elasticity with poultry is low, we cant draw
any conclusions from the results regarding this relationship.
4.5. Model of Poultry meat consumption
4.5.1. Specification of our transformed model
In this chapter we will be using the same data set consisting of 532 annual observations
covering the period from 2000 to 2018 for 28 countries. We will change our dependant
variable to be log(poultry consumption per capita). After log transformation of selected
explanatory variables, we will get the estimates using a simple pooled OLS model.
The Equation of our model:
Log(PoultryPCc,t )= α + β1 log(Average real wage c,t )+ β2 Unemploymentc,t + β3 Young
c,t + β4 Oldc,t + β5 Urbanization c,t + β6 Female labour force sharec,t + β7 log(GDP per
capitac,t)+β8Education c,t + β9 log(Pork Pricec,t )+ β10 log(Beef Pricec,t )+ β11 log(Poultry
Pricec,t )+ β12 Food Percentagec,t +Unobserved effectc + e c,t
4.5.2. Results of our transformed model regarding
Poultry meat consumption
Table 4.6 Log-log/level model of total poultry meat consumption Coefficient
Estimate Std. Error P (>|t|)
Intercept
7,117 0,545 2,2e-16 ***
Log(Average real wage)
0,0944 0,0607 0,12
Unemployment -0,0174 0,00313 4,569e-08 ***
23
Young -0,00491 0,00545 0,369
Old -0.021 0,004 2,223e-07 ***
Urbanization -0,000165 0,00142 0,908
Female labour force share -0,00898 0,00568 0,113
Log(GDP per capita) -0,361 0,0671 1,15e-07 ***
Education 0,00984 0,00314 0,00182 **
Log(Beef Price) 0,104 0,133 0,437
Log(Pork Price) 0,0108 0,0996 0,913
Log(Poultry Price) 0,428 0,153 0,00541 **
Food Percentage -0,0352 0,00571 1,484e-09 ***
R-Squared 0,2873
Note: . p<0,05 ; * p<0,01 ; ** p<0,001 ; *** p < 0
Our estimate of income elasticity regarding poultry meat is not statistically significant.
The only estimate connected to price that is significant is poultry with a value of
0,428, this would make poultry a Giffen good. Consumption of Giffen goods increases
when the price increases and vice versa.
5. Difficulties, Extensions, and Improvements
5.1. Difficulties Regarding Data
Missing values across multiple datasets was a major limiting factor for explanatory
variables of our model, even though a lot of institutions collect data and share them with
the world. Our goal was to save as many observations as possible by approximating
missing data points in time series data.
Even though the quality of data is improving over time, which could be seen
during the data preparation part of our work, we hope that future researchers will have
access to better datasets with more observations, fewer discrepancies in methods used to
collect data and more consistent time series data without missing variables.
One small obstacle during the data preparation process was the inconsistency of
country names between individual data sets, we had to carefully match country names
and unify them.
Another obstacle was the different metrics used by various datasets, we had to be
careful when computing our own variables to not make any mistakes in conversions.
5.2. Possible Extensions and improvements
Possible extension of my work could be adding more information of a microeconomic
character such as data regarding consumer baskets in supermarkets, where the
24
connections between various meat types and their prices can be analysed. This data is
sadly not available publicly because it could lead to other companies gaining competitive
advantage on the market.
Another possibility would be to add more food items into the research, FAOSTAT
collects data regarding production, import, export, change in stock and losses for over
120 items, for example relationship with the consumption of alcohol beverages or with
other animal-based products such as eggs could be researched. Adding more meat types
could also be interesting: fish, donkeys, horses, camels, and goats were discarded in our
research, because they were usually only country specific and their explanatory power in
our framework would be low.
Great improvement would be to obtain country specific data regarding
population’s culture, that we could not obtain, for example data covering religion or the
trend of veganism. Another possibility regarding country specific population variables
would be to merge our research with anthropology and see if meat consumption can be
connected with various ethnicities.
Interesting results could be obtained by adding country specific geographical
features into our research, for instance to see how the country’s shore length relates to
consumption of fish and other marine animals or if more pastures per square kilometre
translates to increased beef meat consumption.
6. Conclusion
Our thesis aimed to recognize and estimate the important determinants of meat production
in the European Union using the panel data analysis. We used data from the year 2000 to
the year 2018, the data came from 28 countries that were members of EU in the year 2018.
The data was aggregated to the detail of countries and was collected annually.
Firstly, we performed fixed effects model estimation regarding the total meat
consumption in the EU. The main goal was to get the estimates of the main factors
influencing meat consumption from the literature and to verify and quantitatively measure
their impact on meat consumption in the European Union. We managed to successfully
link real average wage, unemployment, share of dependants, share of females in the
labour force, GDP per capita, education, price of meat and the percentage houses spent
on food to the meat consumption.
Next, we log transformed our model into constant elasticity model, we also split
the meat consumption and meat price into 3, each representing one specific meat type
(beef, pork and poultry). The goal was to obtain income, price, and cross price elasticities
of the mentioned meat types using simple pooled OLS estimates. Our estimates usually
confirmed existing research and literature in terms of “labelling” of our meat types,
however we did not always arrive at the placement on the scale we would have expected.
This might be caused by the fact, that in order for us to even be able to estimate elasticities,
we need to assume the homogeneity of meat. Every gram of specific meat type would
need to be the same, they would need to be the same price and be demanded equally by
consumers. The nature of our variable representing real prices could also be an issue, our
price was of commodity nature and it deflated the real prices our consumers had to face
at the end of the retail chain. One last thing that could be done to improve the elasticity
estimates would be employing more variables in our model, for example data regarding
the consumer preferences, advertising, or level of caring about the well-being of animals.
Our thesis contributes to the current research regarding the main determinants of
meat consumption by confirming previous findings and linking the suspected variables to
meat consumption. Our results – when paired with data regarding costs of public health
25
could be used by policy makers in the public sector, when designing what the optimal tax
rate on meat (mainly harmful red and processed meat) should be. The results could also
be used by the private sector, for example by companies trying to maximize their profit.
The thesis has successfully continued current research regarding the determinants
of meat consumption and complements existing literature regarding this topic. Goals
introduced in the introduction chapter were fulfilled and the possible improvements and
deeper research goals were outlined.
26
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